Independent Component Analysis–Based Fuel Type Identification for Coal-Fired Power Plants

Tan, C. and Xu, L. and Li, X. and Yan, Y. (2012) Independent Component Analysis–Based Fuel Type Identification for Coal-Fired Power Plants. Combustion Science and Technology, 184 (3). pp. 277-292. ISSN 0010-2202. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1080/00102202.2011.635613

Abstract

Independent component analysis (ICA) and support vector machine (SVM) techniques were used to identify the fuel types. Flame oscillation signals were captured by a flame monitor. Thirty flame features were extracted from each flame oscillation signal to form an original feature vector. The ICA technique was applied to choose the independent flame features from each original feature vector. An SVM model was deployed to map the flame features to an individual type of fuel. The results obtained by using eight different types of coal demonstrated that the ICA technique combining with a well trained SVM can be used for identifying the fuel types, and the average success rate was 96.2% in 20 trials. The ICA preceded by principal component analysis (PCA) used for whitening and dimension-reducing performed a bit better than individually using the ICA technique, and the average success rate of fuel type identification was 97.8% in 20 trials

Item Type: Article
Uncontrolled keywords: Feature extraction, flame feature, fuel type, independent component analysis (ICA), support vector machine (SVM)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: J. Harries
Date Deposited: 25 Apr 2012 14:38
Last Modified: 01 May 2012 15:12
Resource URI: http://kar.kent.ac.uk/id/eprint/29340 (The current URI for this page, for reference purposes)
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